Cardiac Health Diagnosis Using Higher Order Spectra and Support Vector Machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification

Automatic identification of epileptic and background EEG signals using frequency domain parameters

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%

Experience developing a pediatric medical chatbot in Singapore: a digital innovation for improved emergency care

This community case study explores the lessons learnt from the development of the Urgent Paediatric Advice Line (UPAL), a medical chatbot designed to address key challenges in pediatric healthcare, including emergency department (ED) overcrowding, health-seeking behavior, and health literacy. The chatbot was developed by pediatric specialists in collaboration with an AI-driven technology partner to provide caregivers with timely, accurate, and accessible guidance for managing pediatric health concerns. By helping parents assess the severity of their child's symptoms and navigate appropriate care pathways, UPAL aims to reduce unnecessary ED visits and improve health literacy. The development process employed an iterative, user-centered approach to refine the algorithm and enhance the user experience, with key challenges including balancing clinical reliability with user empathy. By offering evidence-based advice tailored to individual symptoms, UPAL empowers caregivers to make more informed decisions about their child's care. This case study highlights the potential of digital health solutions to empower caregivers, improve patient engagement, and increase healthcare access, particularly in pediatric settings. The study underscores the lessons for the field—namely the importance of interdisciplinary collaboration, continuous iterative development, patient-centered design, and active stakeholder engagement in creating effective digital health tools. Looking forward, future developments will include the incorporation of generative AI to provide more humanistic and personalized responses, as well as the creation of a post-discharge outreach module to provide proactive post-discharge support to caregivers, further enhancing healthcare delivery in a rapidly evolving digital landscape

Riverine sustainment 2012

Student Integrated ProjectIncludes supplementary materialThis technical report analyzed the Navy's proposed Riverine Force (RF) structure and capabilities for 2012. The Riverine Sustainment 2012 Team (RST) examined the cost and performance of systems of systems which increased RF sustainment in logistically barren environments. RF sustainment was decomposed into its functional areas of supply, repair, and force protection. The functional and physical architectures were developed in parallel and were used to construct an operational architecture for the RF. The RST used mathematical, agent-based and queuing models to analyze various supply, repair and force protection system alternatives. Extraction of modeling data revealed several key insights. Waterborne heavy lift connectors such as the LCU-2000 are vital in the re-supply of the RF when it is operating up river in a non-permissive environment. Airborne heavy lift connectors such as the MV-22 were ineffective and dominated by the waterborne variants in the same environment. Increase in manpower and facilities did appreciable add to the operational availability of the RF. Mean supply response time was the biggest factor effecting operational availability and should be kept below 24 hours to maintain operational availability rates above 80%. Current mortar defenses proposed by the RF are insufficient.N

Determination Of Optimal Storage Reservoir Location Considering Regional Importance

Urban drainage systems are affected by various regional characteristics such as sewer properties, misaligned connections, manhole ground level, and design flood stage at outlet. Therefore, combinatorial characteristic of selecting optimal location of storage facilities from many candidate locations gets even more complicated and becomes a challenging task. The process of selection is conducted using system analyses to address the effects that provide for high efficiency and low damage. This paper focuses on developing an optimal design model for determination of the storage reservoir locations considering regional importance. For cases of urban inundation, importance of flooded region considering the damage needs to be included in the determination of reservoir locations. A multi-dimensional flood damage analysis is employed for each important basin, where the higher the calculated cost of damage obtained by this method, the more important it is and thus, the greater the need for installation of a reservoir to prevent flooding. Flooding damage costs according to overflow volume were obtained by repeated precipitation-runoff analyses with overflow depth calculations. The depth calculation and a damage function analysis are combined using the multi-dimensional method and applied to urban watersheds in Korea. In conclusion, the approach of this paper can be considered as a methodology for the determination of storage reservoir location to reduce inland flooding and damage

Comparison of Quantitative Cytomegalovirus Real-time PCR in Whole Blood and pp65 Antigenemia Assay: Clinical Utility of CMV Real-time PCR in Hematopoietic Stem Cell Transplant Recipients

Successful preemptive therapy for cytomegalovirus (CMV) infection in transplant patients depends on the availability of sensitive, specific, and timely diagnostic tests for CMV infection. Although the pp65 antigenemia assay has been widely used for this purpose, real-time quantification of CMV DNA has recently been recognized as an alternative diagnostic approach. However, the guidelines for antiviral therapy based on real-time quantitative polymerase chain reaction (RQ-PCR) have yet to be established. From November 2004 to March 2005, a total of 555 whole blood samples from 131 hematopoietic stem cell transplant (HSCT) recipients were prospectively collected. RQ-PCR was conducted using an Artus® CMV LC PCR kit (QIAGEN). Both qualitative and quantitative correlations were drawn between the two methods. Exposure to the antiviral agent influenced the results of the two assays. Additionally, the discrepancy was observed at low levels of antigenemia and CMV DNA load. Via ROC curve analysis, the tentative cutoff value for preemptive therapy was determined to be approximately 2×104 copies/mL (sensitivity, 80.0%; specificity, 50.0%) in the high risk patients, and approximately 3×104 copies/mL (sensitivity, 90.0%; specificity, 70.0%) in the patients at low risk for CMV disease. Further study to validate the optimal cutoff value for the initiation of preemptive therapy is currently underway

Analysis of cardiac signals using spatial filling index and time-frequency domain

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%

APOE Promoter Polymorphism-219T/G is an Effect Modifier of the Influence of APOE ε4 on Alzheimer's Disease Risk in a Multiracial Sample

Variants in the APOE gene region may explain ethnic differences in the association of Alzheimer's disease (AD) with ε4. Ethnic differences in allele frequencies for three APOE region SNPs (single nucleotide polymorphisms) were identified and tested for association in 19,398 East Asians (EastA), including Koreans and Japanese, 15,836 European ancestry (EuroA) individuals, and 4985 African Americans, and with brain imaging measures of cortical atrophy in sub-samples of Koreans and EuroAs. Among ε4/ε4 individuals, AD risk increased substantially in a dose-dependent manner with the number of APOE promoter SNP rs405509 T alleles in EastAs (TT: OR (odds ratio) = 27.02, p = 8.80 × 10-94; GT: OR = 15.87, p = 2.62 × 10-9) and EuroAs (TT: OR = 18.13, p = 2.69 × 10-108; GT: OR = 12.63, p = 3.44 × 10-64), and rs405509-T homozygotes had a younger onset and more severe cortical atrophy than those with G-allele. Functional experiments using APOE promoter fragments demonstrated that TT lowered APOE expression in human brain and serum. The modifying effect of rs405509 genotype explained much of the ethnic variability in the AD/ε4 association, and increasing APOE expression might lower AD risk among ε4 homozygotes